Sensitivity of WRF short-term forecasts to different soil moisture initializations from the GLDAS database over South America in March 2009

2016 ◽  
Vol 167 ◽  
pp. 196-207 ◽  
Author(s):  
María E. Dillon ◽  
Estela A. Collini ◽  
Lorena J. Ferreira
Keyword(s):  
Soil Moisture ◽  
South America ◽  
Short Term

scholarly journals Interactive effects of ambient ozone and climate measured on growth of mature loblolly pine trees

1996 ◽  
Vol 26 (4) ◽  
pp. 670-681 ◽  
Author(s):  
S.B. McLaughlin ◽  
D.J. Downing
Keyword(s):  
Soil Moisture ◽  
Loblolly Pine ◽  
Moisture Stress ◽  
Growth Patterns ◽  
Forest Growth ◽  
Interactive Effects ◽  
Forest Trees ◽  
Short Term ◽  
Data Set ◽  
Ambient Ozone

Seasonal growth patterns of mature loblolly pine (Pinustaeda L.) trees over the interval 1988–1993 have been analyzed to evaluate the effects of ambient ozone on growth of large forest trees. Patterns of stem expansion and contraction of 34 trees were examined using serial measurements with sensitive dendrometer band systems. Study sites, located in eastern Tennessee, varied significantly in soil moisture, soil fertility, and stand density. Levels of ozone, rainfall, and temperature varied widely over the 6-year study interval. Regression analysis identified statistically significant influences of ozone on stem growth patterns, with responses differing widely among trees and across years. Ozone interacted with both soil moisture stress and high temperatures, explaining 63% of the high frequency, climatic variance in stem expansion identified by stepwise regression of the 5-year data set. Observed responses to ozone were rapid, typically occurring within 1–3 days of exposure to ozone at ≥40 ppb and were significantly amplified by low soil moisture and high air temperatures. Both short-term responses, apparently tied to ozone-induced increases in whole-tree water stress, and longer term cumulative responses were identified. These data indicate that relatively low levels of ambient ozone can significantly reduce growth of mature forest trees and that interactions between ambient ozone and climate are likely to be important modifiers of future forest growth and function. Additional studies of mechanisms of short-term response and interspecies comparisons are clearly needed.

scholarly journals An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis

2021 ◽  
Vol 3 ◽  
Author(s):  
Yueling Ma ◽  
Carsten Montzka ◽  
Bagher Bayat ◽  
Stefan Kollet
Keyword(s):  
Soil Moisture ◽  
Indirect Method ◽  
Short Term Memory ◽  
Groundwater Table ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Continental Scale ◽  
Groundwater Table Depth ◽  
Long Short Term Memory

The lack of high-quality continental-scale groundwater table depth observations necessitates developing an indirect method to produce reliable estimation for water table depth anomalies (wtda) over Europe to facilitate European groundwater management under drought conditions. Long Short-Term Memory (LSTM) networks are a deep learning technology to exploit long-short-term dependencies in the input-output relationship, which have been observed in the response of groundwater dynamics to atmospheric and land surface processes. Here, we introduced different input variables including precipitation anomalies (pra), which is the most common proxy of wtda, for the networks to arrive at improved wtda estimates at individual pixels over Europe in various experiments. All input and target data involved in this study were obtained from the simulated TSMP-G2A data set. We performed wavelet coherence analysis to gain a comprehensive understanding of the contributions of different input variable combinations to wtda estimates. Based on the different experiments, we derived an indirect method utilizing LSTM networks with pra and soil moisture anomaly (θa) as input, which achieved the optimal network performance. The regional medians of test R2 scores and RMSEs obtained by the method in the areas with wtd ≤ 3.0 m were 76–95% and 0.17–0.30, respectively, constituting a 20–66% increase in median R2 and a 0.19–0.30 decrease in median RMSEs compared to the LSTM networks only with pra as input. Our results show that introducing θa significantly improved the performance of the trained networks to predict wtda, indicating the substantial contribution of θa to explain groundwater anomalies. Also, the European wtda map reproduced by the method had good agreement with that derived from the TSMP-G2A data set with respect to drought severity, successfully detecting ~41% of strong drought events (wtda ≥ 1.5) and ~29% of extreme drought events (wtda ≥ 2) in August 2015. The study emphasizes the importance to combine soil moisture information with precipitation information in quantifying or predicting groundwater anomalies. In the future, the indirect method derived in this study can be transferred to real-time monitoring of groundwater drought at the continental scale using remotely sensed soil moisture and precipitation observations or respective information from weather prediction models.

Wildfire and Postfire Restoration Action Effects on Microclimate and Seedling Pine Tree Survivorship

2014 ◽  
Vol 5 (1) ◽  
pp. 174-182 ◽  
Author(s):  
Donald J. Brown ◽  
Ivana Mali ◽  
Michael R.J. Forstner
Keyword(s):  
Soil Moisture ◽  
Wind Speed ◽  
Soil Temperature ◽  
Air Temperature ◽  
Loblolly Pine ◽  
Absolute Humidity ◽  
Short Term ◽  
Burned Areas ◽  
High Severity ◽  
Postfire Management

Abstract Through modification of structural characteristics, ecological processes such as fire can affect microhabitat parameters, which in turn can influence community composition dynamics. The prevalence of high-severity forest fires is increasing in the southern and western United States, creating the necessity to better understand effects of high-severity fire, and subsequent postfire management actions, on forest ecosystems. In this study we used a recent high-severity wildfire in the Lost Pines ecoregion of Texas to assess effects of the wildfire and postfire clearcutting on six microclimate parameters: air temperature, absolute humidity, mean wind speed, maximum wind speed, soil temperature, and soil moisture. We also assessed differences between burned areas and burned and subsequently clearcut areas for short-term survivorship of loblolly pine Pinus taeda seedling trees. We found that during the summer months approximately 2 y after the wildfire, mean and maximum wind speed differed between unburned and burned areas, as well as burned and burned and subsequently clearcut areas. Our results indicated air temperature, absolute humidity, soil temperature, and soil moisture did not differ between unburned and burned areas, or burned and burned and subsequently clearcut areas, during the study period. We found that short-term survivorship of loblolly pine seedling trees was influenced primarily by soil type, but was also lower in clearcut habitat compared with habitat containing dead standing trees. Ultimately, however, the outcome of the reforestation initiative will likely depend primarily on whether or not the trees can survive drought conditions in the future, and this study indicates there is flexibility in postfire management options prior to reseeding. Further, concerns about negative wildfire effects on microclimate parameters important to the endangered Houston toad Bufo (Anaxyrus) houstonensis were not supported in this study.

Influence of soil moisture on short-term adsorption of diuron and isoproturon by soil

1995 ◽  
Vol 45 (4) ◽  
pp. 297-303 ◽  
Author(s):  
Paul Gaillardon ◽  
Jeanne C. Dur
Keyword(s):  
Soil Moisture ◽  
Short Term

scholarly journals Soil carbon release responses to long-term versus short-term climatic warming in an arid ecosystem

2020 ◽  
Vol 17 (3) ◽  
pp. 781-792 ◽  
Author(s):  
Hongying Yu ◽  
Zhenzhu Xu ◽  
Guangsheng Zhou ◽  
Yaohui Shi
Keyword(s):  
Soil Moisture ◽  
Soil Carbon ◽  
Field Experiments ◽  
Sigmoid Function ◽  
Desert Grassland ◽  
Climatic Warming ◽  
Short Term ◽  
Exponential Relationship ◽  
Carbon Release

Abstract. Climate change severely impacts the grassland carbon cycling by altering rates of litter decomposition and soil respiration (Rs), especially in arid areas. However, little is known about the Rs responses to different warming magnitudes and watering pulses in situ in desert steppes. To examine their effects on Rs, we conducted long-term moderate warming (4 years, ∼3 ∘C), short-term acute warming (1 year, ∼4 ∘C) and watering field experiments in a desert grassland of northern China. While experimental warming significantly reduced average Rs by 32.5 % and 40.8 % under long-term moderate and short-term acute warming regimes, respectively, watering pulses (fully irrigating the soil to field capacity) stimulated it substantially. This indicates that climatic warming constrains soil carbon release, which is controlled mainly by decreased soil moisture, consequently influencing soil carbon dynamics. Warming did not change the exponential relationship between Rs and soil temperature, whereas the relationship between Rs and soil moisture was better fitted to a sigmoid function. The belowground biomass, soil nutrition, and microbial biomass were not significantly affected by either long-term or short-term warming regimes, respectively. The results of this study highlight the great dependence of soil carbon emission on warming regimes of different durations and the important role of precipitation pulses during the growing season in assessing the terrestrial ecosystem carbon balance and cycle.

scholarly journals Short-Term and Long-Term Surface Soil Moisture Memory Time Scales Are Spatially Anticorrelated at Global Scales

2019 ◽  
Vol 20 (6) ◽  
pp. 1165-1182 ◽  
Author(s):  
Kaighin A. McColl ◽  
Qing He ◽  
Hui Lu ◽  
Dara Entekhabi
Keyword(s):  
Soil Moisture ◽  
Time Scales ◽  
Time Scale ◽  
Surface Soil ◽  
Satellite Observations ◽  
Surface Soil Moisture ◽  
Short Term ◽  
Term Memory ◽  
Memory Time

Abstract Land–atmosphere feedbacks occurring on daily to weekly time scales can magnify the intensity and duration of extreme weather events, such as droughts, heat waves, and convective storms. For such feedbacks to occur, the coupled land–atmosphere system must exhibit sufficient memory of soil moisture anomalies associated with the extreme event. The soil moisture autocorrelation e-folding time scale has been used previously to estimate soil moisture memory. However, the theoretical basis for this metric (i.e., that the land water budget is reasonably approximated by a red noise process) does not apply at finer spatial and temporal resolutions relevant to modern satellite observations and models. In this study, two memory time scale metrics are introduced that are relevant to modern satellite observations and models: the “long-term memory” τL and the “short-term memory” τS. Short- and long-term surface soil moisture (SSM) memory time scales are spatially anticorrelated at global scales in both a model and satellite observations, suggesting hot spots of land–atmosphere coupling will be located in different regions, depending on the time scale of the feedback. Furthermore, the spatial anticorrelation between τS and τL demonstrates the importance of characterizing these memory time scales separately, rather than mixing them as in previous studies.

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